project_6

Files

EngineX CI 56fd68e7dd [INFRA] Import NVIDIA/CCCL upstream as optimization reference library

CCCL (CUDA C++ Core Libraries) provides:
- CUB: device/block/warp-level GPU primitives (reduce, scan, sort, topk)
- Thrust: high-level parallel algorithms (transform_reduce, sort, scan)
- libcudacxx: CUDA C++ standard library (atomics, barriers, memory)
- cudax: experimental features (memory resources, allocators)
- Tuning policies: per-SM hardware-specific algorithm parameters

Competition optimization vectors mapped to CCCL:
- Output TPS (83% weight): warp_reduce, block_reduce, device_topk
- Input TPS (14% weight): device_scan, block_load, prefetch
- Cache TPS (3% weight): prefix caching strategy patterns
- Memory (0.9 util): pooled/cached/buddy allocators

Source: https://github.com/NVIDIA/cccl (shallow clone, HEAD only)
License: Apache-2.0

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cpp_integration

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cuda

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include

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arbitrary_transformation.cu

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basic_vector.cu

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bounding_box.cu

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bucket_sort2d.cu

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CMakeLists.txt

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constant_iterator.cu

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counting_iterator.cu

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device_ptr.cu

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discrete_voronoi.cu

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dot_products_with_zip.cu

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expand.cu

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fill_copy_sequence.cu

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histogram.cu

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lambda.cu

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lexicographical_sort.cu

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max_abs_diff.cu

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minimal_custom_backend.cu

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minmax.cu

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mode.cu

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monte_carlo_disjoint_sequences.cu

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monte_carlo.cu

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mr_basic.cu

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norm.cu

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padded_grid_reduction.cu

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permutation_iterator.cu

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print_version.cu

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raw_reference_cast.cu

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README.md

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remove_points2d.cu

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repeated_range.cu

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run_length_decoding.cu

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run_length_encoding.cu

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saxpy.cu

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scan_by_key.cu

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scan_matrix_by_rows.cu

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set_operations.cu

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simple_moving_average.cu

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sort.cu

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sorting_aos_vs_soa.cu

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sparse_vector.cu

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stream_compaction.cu

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strided_range.cu

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sum_columns.cmake

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sum_columns.cu

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sum_rows.cmake

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sum_rows.cu

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sum.cu

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summary_statistics.cu

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summed_area_table.cu

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tiled_range.cu

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transform_input_output_iterator.cu

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transform_iterator.cu

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transform_output_iterator.cu

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uninitialized_vector.cu

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weld_vertices.cu

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word_count.cmake

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word_count.cu

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README.md

Once Thrust has been installed, these example programs can be compiled directly with nvcc. For example, the following command will compile the norm example.

$ nvcc norm.cu -o norm

These examples are also available online: https://github.com/NVIDIA/cccl/tree/main/thrust/examples

For any serious experimentation, we recommend using CMake and CCCL from GitHub. We also provide consistent and convenient development environments as devcontainers.